AARHUS UNIVERSITY
4-5 May
Moesgaard Campus
A Conference on the Archaeological Applications of Morphometrics
5/4/2017
AARHUS UNIVERSITY
4-5 May
Moesgaard Campus
A Conference on the Archaeological Applications of Morphometrics
Rogers (1999); Falys et al (2005).
Rogers (1999); Falys et al (2005).
Rogers (2009)
Vance et al. 2011
Rogers (1999); Falys et al (2005).
The first evaluated the constriction of the trochlea, with the curve reaching from the most medial inferior aspect to the most lateral inferior aspect of the trochlea. The second described the outline of the olecranon fossa and was recorded clockwise starting from its most inferior aspect.
Individual 4 (female) and Individual 6 (male) of the CEI/XXI collection.
From then on, both sets of data were processed with the same statistical algorithms.
Hereby, both sets of data were processed with the same statistical algorithms.
This follows the recent trend in forensic anthropology, which attempts to make complicated statistical models more accessible and easier to utilize through interactive online applications (e.g. Navega et al., 2015; Gonçalves et al, 2016; Curate et al, 2016).
TC.n <- npoly(TC.aligned, nb.pts = 16, degree = 8) TC.pca <- PCA(TC.n) TC.pca %>% plot(., "Sex", col = palet)
TC.pca %>% as_df() %>% ggplot() + aes(x = PC1, y = PC2, col = Sex) +
geom_point() + geom_density2d() + theme_light()
TC.ms <- mshapes(TC.n, 1)
TC_female <- TC.ms$shp$Female %T>% coo_plot(border = palet[1])
TC_male <- TC.ms$shp$Male %T>% coo_draw(border = palet[2])
legend("topright", lwd = 1, col = palet, legend = c("Female", "Male"), cex = 0.8)
tps_grid(TC_female, TC_male, shp.border = palet)
tps_arr(TC_female, TC_male, shp.border = palet)
tps_iso(TC_female, TC_male, shp.border = palet)
MANOVA(TC.pca, 1)
## PC axes 1 to 2 were retained
## Df Hotelling-Lawley approx F num Df den Df Pr(>F) ## fac 1 0.031687 2.3448 2 148 0.09942 . ## Residuals 149 ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TC.lda <- LDA(TC.pca, 1)
## 0.99 total variance
## 2 PC retained
TC.lda
## * Leave-one-out cross-validation ($CV.correct): (60.9% - 92/151): ## ## * Class correctness ($CV.ce): ## Female Male ## 0.7875000 0.4084507 ## ## * Cross-validation table ($CV.tab): ## classified ## actual Female Male ## Female 63 17 ## Male 42 29
plot(TC.lda, palette = cores2)
## NULL
OF.ef <- efourier(OF.aligned, nb.h = 6) OF.pca <- PCA(OF.ef) OF.pca %>% plot(., "Sex", col = palet)
OF.pca %>% as_df() %>% ggplot() + aes(x = PC1, y = PC2, col = Sex) + coord_equal() +
geom_point() + geom_density2d() + theme_light()
OF.ms <- mshapes(OF.ef, 1)
OF_female <- OF.ms$shp$Female %T>% coo_plot(border = palet[1])
OF_male <- OF.ms$shp$Male %T>% coo_draw(border = palet[2])
legend("topright", lwd = 1, col = palet, legend = c("Female", "Male"), cex = 0.8)
tps_grid(OF_female, OF_male, shp.border = palet)
tps_arr(OF_female, OF_male, shp.border = palet)
tps_iso(OF_female, OF_male, grid = TRUE, shp.border = palet)
MANOVA(OF.pca, 1)
## PC axes 1 to 7 were retained
## Df Hotelling-Lawley approx F num Df den Df Pr(>F) ## fac 1 1.8666 38.133 7 143 < 2.2e-16 *** ## Residuals 149 ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
OF.lda <- LDA(OF.pca, 1)
## 0.99 total variance
## 7 PC retained
OF.lda
## * Leave-one-out cross-validation ($CV.correct): (92.1% - 139/151): ## ## * Class correctness ($CV.ce): ## Female Male ## 0.9500000 0.8873239 ## ## * Cross-validation table ($CV.tab): ## classified ## actual Female Male ## Female 76 4 ## Male 8 63
plot(OF.lda, palette = cores2)
## NULL
By better we mean:
cor(OF.pca$x[,1:7], as.numeric(OF.pca$fac$Sex)) # correlations
## [,1] ## PC1 0.05302026 ## PC2 0.46336920 ## PC3 -0.11872418 ## PC4 0.63185511 ## PC5 -0.09388462 ## PC6 0.05482801 ## PC7 0.09208552
Let's see if it makes sense. Little exclusive for Morph2017
OF.pca %>% as_df() %>% ggplot() + aes(x = PC2, y = PC4, col = Sex) +
geom_point() + geom_density2d() + theme_light()
OF.lda2 <- LDA(OF.pca$x[,c(2,4)], OF.pca$fac$Sex) OF.lda2
## * Leave-one-out cross-validation ($CV.correct): (91.4% - 138/151): ## ## * Class correctness ($CV.ce): ## Female Male ## 0.9375000 0.8873239 ## ## * Cross-validation table ($CV.tab): ## classified ## actual Female Male ## Female 75 5 ## Male 8 63
This has been implemented in an interactive, easy-to-use, free to all, way: